What is “Liveness”?
Biometric detractors argue, "You can reset your password if stolen, but you can't reset your face". While this is true, it is a failure of imagination to stop there. We must ask, "What would make centralized biometric authentication safe?"
Liveness Detection prevents bots and bad actors from using photos, videos, masks, or other biometric data (stolen or otherwise) to create or access online accounts. Liveness ensures only real humans can create and access accounts.
Liveness checks solve some very serious problems. For example, Facebook had to delete 2.2 billion fake accounts in 2019 alone! Requiring proof of Liveness would have prevented these fakes from ever being created.
Requiring every new user prove their Liveness before they are even asked to present an ID Document during the digital onboarding process is in itself a huge deterrent to fraudsters who don't ever want their Real Face on camera.
Liveness Data should be timestamped so it is only valid for few minutes, and then deleted. New Liveness Data must be collected for every authentication attempt. Only User Biometric Data should ever be saved, never Liveness Data.
Just as photos from LinkedIn or Instagram can’t spoof Certified Liveness Detection, neither can the standalone User Biometric Data. By deleting the Liveness Data and only storing the User Biometric Data, there is no honeypot risk.
Note: Think of the stored User Biometric Data as the lock, the newly collected User Biometric Data as a one-time-use key, and the Liveness Data as proof the key has never been used before.
Due to "hill-climbing" attacks, biometric systems should never reveal which part of the system did or didn't catch a spoof, and while ISO 30107-3 gets a lot right, it unfortunately encourages testing both Liveness and Matching at the same time. Scientific method requires the fewest variables possible be tested at once, so Liveness testing should be done with a solely Boolean (true/false) response. Tests should not allow systems to have multiple-decision layers that could allow an artifact to pass Liveness but fail Matching because it didn't look enough like the enrolled subject.
The Problem With CAPTCHAs
Jason Polakis, a computer scientist, used off-the-shelf image recognition tools, including Google's own image search, to solve Google's image CAPTCHA with 70% accuracy, states “You need something that’s easy for an average human, it shouldn’t be bound to a specific subgroup of people, and it should be hard for computers at the same time.”
Even without AI, services like: deathbycaptcha.com and anti-captcha.com allow bots to bypass the challenge–responses tests by using proxy humans to complete them. With so many people willing to do this work, it's cheap to defeat at scale and workers earn between $0.25-$0.60 for every 1000 CAPTCHAs solved. (webemployed).
Gartner, “Presentation attack detection (PAD, a.k.a., “liveness testing”) is a key selection criterion. ISO/IEC 30107 “Information Technology — Biometric Presentation Attack Detection” was published in 2017.
1:1 (1-to-1) – Comparing the biometric data from a subject User to the biometric data stored for the expected User. If the biometric data does not match above the chosen FAR level, the result is a failed match.
Artifact (Artefact) – An inanimate object that seeks to reproduce human biometric traits.
Authentication – The concurrent Liveness Detection, 3D depth detection, and biometric data verification (i.e., face sharing) of the User.
Bad Actor – A criminal; a person with intentions to commit fraud by deceiving others.
Biometric – The measurement and comparison of data representing the unique physical traits of an individual for the purposes of identifying that individual based on those unique traits.
Certification – The testing of a system to verify its ability to meet or exceed a specified performance standard. Testing labs Like iBeta issue certifications.
Complicit User Fraud – When a User pretends to have fraud perpetrated against them, but has been involved in a scheme to defraud by stealing an asset and trying to get it replaced by an institution.
Cooperative User – When a testing organization is guided by ISO 30107-3 ISO, the human Subjects used in the tests must provide any and all biometric data that is requested. This helps to assess the complicit User fraud and phishing risk, but only applies if the test includes matching (not recommended).
Centralized Biometric – Biometric data is collected on any supported device, encrypted and sent to a server for enrollment and later authentication for that device or any other supported device. When the User’s original biometric data is stored on a secure 3rd-party server, that data can continue to be used as the source of trust and their identity can be established and verified at any time. Any supported device can be used to collect and send biometric data to the server for comparison, enabling Users to access their accounts from all of their devices, new devices, etc., just like with passwords. Liveness is the most critical component of a centralized biometric system, and because certified Liveness did not exist until recently, centralized biometrics have not yet been widely deployed.
Credential Sharing – When two or more individuals do not keep their credentials secret and can access each others accounts. This can be done to subvert licensing fees or to trick an employer into paying for time not worked (also called “buddy punching”).
Credential Stuffing – A cyberattack where stolen account credentials, usually comprising lists of usernames and/or email addresses and the corresponding passwords, are used to gain unauthorized user account access.
Decentralized Biometric – When biometric data is captured and stored on a single device and the data never leaves that device. Fingerprint readers in smartphones and Apple’s Face ID are examples of decentralized biometrics. They only unlock one specific device, they require re-enrollment on any new device, and further do not prove the identity of the User whatsoever. Decentralized biometric systems can be defeated easily if a bad actor knows the device's override PIN number, allowing them to overwrite the User’s biometric data with their own.
End User– An individual human who is using an application.
Enrollment – When biometric data is collected for the first time, encrypted and sent to the server. Note: Liveness must be verified and a 1:N check should be performed against all the other enrollments to check for duplicates.
Face Authentication – Authentication has three parts: Liveness Detection, 3D Depth Detection and Identity Verification. All must be done concurrently on the same face frames.
Face Matching – Newly captured images/biometric data of a person are compared to the enrolled (previously saved) biometric data of the expected User, determining if they are the same.
Face Recognition – Images/biometric data of a person are compared against a large list of known individuals to determine if they are the same person.
FIDO – Stands for Fast IDentity Online: A standards organization that provides guidance to organization that choose to use Decentralized Biometric Systems (https://fidoalliance.org).
FRR/FNMR/FMR – The probability that a system will reject the correct User when that User’s biometric data is presented to the sensor. If the FRR is high, Users will be frustrated with the system because they are prevented from accessing their own accounts.
Hill-Climbing Attack – When an attacker uses information returned by the biometric authenticator (match level or liveness score) to learn how to curate their attacks and gain a higher probability of spoofing the system.
iBeta – A NIST-certified testing lab in Denver Colorado; the only lab currently certifying biometric systems for anti-spoofing/Liveness Detection to the ISO 30107-3 standard (ibeta.com).
Identity & Access Management (IAM) – A framework of policies and technologies to ensure only authorized users have the appropriate access to restricted technology resources, services, physical locations and accounts. Also called identity management (IdM).
Imposter – A living person with traits so similar to the Subject User that the system determines the biometric data is from the same person.
ISO 30107-3 – The International Organization for Standardization’s testing guidance for evaluation of Anti-Spoofing technology (www.iso.org/standard/67381.html).
Knowledge-Based Authentication (KBA) - Authentication method that seeks to prove the identity of someone accessing a digital service. KBA requires knowing a user's private information to prove that the person requesting access is the owner of the digital identity. Static KBA is based on a pre-agreed set of shared secrets. Dynamic KBA is based on questions generated from additional personal information.
Liveness Detection – The ability for a biometric system to determine if data has been collected from a live human or an inanimate, non-living Artifact.
NIST – National Institute of Standards and Technology – The U.S. government agency that provides measurement science, standards, and technology to advance economic advantage in business and government (nist.gov).
Phishing – When a User is tricked into giving a Bad Actor their passwords, PII, credentials, or biometric data. Example: A User gets a phone call from a fake customer service agent and they request the User’s password to a specific website.
PII – Personally Identifiable Information is information that can be used on its own or with other information to identify, contact, or locate a single person, or to identify an individual in context (en.wikipedia.org/wiki/Personally_identifiable_information).
Presentation Attack Detection (PAD) – A framework for detecting presentation attack events. Related to Liveness Detection and Anti-Spoofing.
Root Identity Provider – An organization that stores biometric data appended to the corresponding personal information of individuals, and allows other organizations to verify the identities of Subject Users by providing biometric data to the Root Identity Provider for comparison.
Spoof – When a non-living object that exhibits some biometric traits is presented to a camera or biometric sensor. Photos, masks or dolls are examples of Artifacts used in spoofs.
Subject User – The individual that is presenting their biometric data to the biometric sensor at that moment.
Synthetic Identity - When a bad actor uses a combination of biometric data, name, social security number, address, etc. to create a new record for a person who doesn't actually exist, for the purposes of using an account in that name.